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• W1998916618 abstract "Brain–machine interfaces have great potential for the development of neuroprosthetic applications to assist patients suffering from brain injury or neurodegenerative disease. One type of brain–machine interface is a cortical motor prosthetic, which is used to assist paralyzed subjects. Motor prosthetics to date have typically used the motor cortex as a source of neural signals for controlling external devices. The review will focus on several new topics in the arena of cortical prosthetics. These include using: recordings from cortical areas outside motor cortex; local field potentials as a source of recorded signals; somatosensory feedback for more dexterous control of robotics; and new decoding methods that work in concert to form an ecology of decode algorithms. These new advances promise to greatly accelerate the applicability and ease of operation of motor prosthetics. Brain–machine interfaces have great potential for the development of neuroprosthetic applications to assist patients suffering from brain injury or neurodegenerative disease. One type of brain–machine interface is a cortical motor prosthetic, which is used to assist paralyzed subjects. Motor prosthetics to date have typically used the motor cortex as a source of neural signals for controlling external devices. The review will focus on several new topics in the arena of cortical prosthetics. These include using: recordings from cortical areas outside motor cortex; local field potentials as a source of recorded signals; somatosensory feedback for more dexterous control of robotics; and new decoding methods that work in concert to form an ecology of decode algorithms. These new advances promise to greatly accelerate the applicability and ease of operation of motor prosthetics. A brain–machine interface (BMI) is a system that can interface the brain with computers and other electronics and can be used to assist subjects with neurological deficits. These devices can ‘write-in’ signals, typically through electrical stimulation; examples include cochlear implants to restore hearing or retinal implants to restore sight. Alternatively, BMIs can ‘read-out’ brain activity, often at the level of action potentials from populations of neurons. A prime example of a read-out BMI is a cortical neural prosthetic used to assist paralyzed patients. This prosthetic records neural activity, decodes the subject’s intent, and then uses this processed intention signal to control external devices such as a computer, robotic limb, or wheelchair. In this review, we shall concentrate on cortical neuroprosthetics for motor control: that is, prosthetics that assist paralyzed subjects. Motor prosthetics primarily rely on reading out neural activity, but they can also write-in feedback signals such as somatosensory feedback. Motor prosthetics to date have typically used the motor cortex and have been previously reviewed [1Hatsopoulos N.G. Donoghue J.P. The science of neural interface systems.Annu. Rev. Neurosci. 2009; 32: 249-266Crossref PubMed Scopus (170) Google Scholar, 2Lebedev M.A. Nicolelis M.A. Brain-machine interfaces: past, present and future.Trends Neurosci. 2006; 29: 536-546Abstract Full Text Full Text PDF PubMed Scopus (0) Google Scholar, 3Schwartz A.B. Cortical neural prosthetics.Annu. Rev. Neurosci. 2004; 27: 487-507Crossref PubMed Scopus (0) Google Scholar]. We shall focus on several new topics in the arena of cortical prosthetics. Firstly, expanding the source of control signals to areas outside motor cortex: current motor prosthetics have used signals recorded primarily from the limb representation in motor cortex. This cortical region is close to the motor output and is concerned with the coding of trajectories of limb movements. Areas that carry the intent to make movements at a higher, cognitive level, such as the posterior parietal cortex (PPC), may allow more intuitive and versatile control. Areas specialized for higher cognitive functions of speech and categorical decision-making may likewise be more adept at controlling a variety of devices from speech synthesizers to computer tablets. Secondly, using local field potentials (LFPs) as a source of recorded signals: whereas spikes are the standard signal for BMIs, LFPs can provide both complementary and supporting signals. A primary hurdle of current prosthetic recordings is the loss of spiking activity with time. LFPs, which are recorded from a larger ‘listening sphere’ than spikes, may extend the lifetime of array implants. Thirdly, the use of somatosensory feedback for more dexterous control of robotics: current prosthetic applications in humans are only of the ‘read-out’ variety and rely on vision for feedback. For patients with spinal cord and other lesions who suffer from both paralysis and loss of somatosensation, writing-in somatosensory signals from electrical stimulation of cortex promises to provide an additional feedback pathway for improving prosthetic performance. And lastly, the development of new decoding methods that work in concert with the decode algorithm to form a decoding ecology. These ecologies contain a mixture of components that optimize training, calibration, feature and parameter selection, match effector dynamics, and estimate intentions. Motor cortex has the attractive feature of being close to the motor output and thus can provide signals highly correlated with desired movement trajectories and the degrees-of-freedom of robotic limbs. However, many of the operations of an external device, such as a robotic limb, do not require all the details of a movement be derived from brain signals to provide dexterous operation. Smart robotics and computer vision can assist in executing the intent of the subjects determined by recordings from cortical areas upstream of the motor cortex to drive prosthetic devices [4Katyal, K.D., Johannes, M.S., McGee, T.G., Harris, A.J., Armiger, R.S., Firpi, A.H., McMullen, D., Hotson, G., Fifer, M.S., Crone, N.E., et al. (2013). HARMONIE: A multimodal control framework for human assistive robotics. In Proc. 6th Int. IEEE/EMBS Conf. on Neural Eng., pp. 1274–1278.Google Scholar]. Moreover, because the intent encoded in these upstream areas is more general than in motor cortex, they may be more flexible and intuitive for prosthetic operations [5Andersen R.A. Burdick J.W. Musallam S. Pesaran B. Cham J.G. Cognitive neural prosthetics.Trends Cogn. Sci. 2004; 8: 486-493Abstract Full Text Full Text PDF PubMed Scopus (75) Google Scholar, 6Andersen R.A. Hwang E.J. Mulliken G.H. Cognitive neural prosthetics.Annu. Rev. Psychol. 2010; 61 (C161–C163): 169-190Crossref PubMed Scopus (0) Google Scholar]. In this section, areas of the posterior parietal cortex (PPC), the premotor cortex, and language areas will be discussed as candidate sites for recording signals for prosthetic applications. The PPC, particularly regions around the intraparietal sulcus (IPS) and superior parietal lobule (SPL), provides a bridge between sensory areas in the caudal cortex and motor areas in more rostral cortex. Neurons in this region cannot be classified as simply sensory or motor, but rather they have properties of both and are involved in sensorimotor transformations. Four general features of the PPC are particularly advantageous for prosthetic control. First, the cells often encode the goal of movements. This goal specificity is advantageous for rapid operations [7Musallam S. Corneil B.D. Greger B. Scherberger H. Andersen R.A. Cognitive control signals for neural prosthetics.Science. 2004; 305: 258-262Crossref PubMed Scopus (463) Google Scholar] and for constraining decode algorithms when used in combination with trajectory signals which are also present in PPC [8Mulliken G.H. Musallam S. Andersen R.A. Decoding trajectories from posterior parietal cortex ensembles.J. Neurosci. 2008; 28: 12913-12926Crossref PubMed Scopus (0) Google Scholar]. Second, the representation of limb movements is bilateral [9Chang S.W. Snyder L.H. The representations of reach endpoints in posterior parietal cortex depend on which hand does the reaching.J. Neurophysiol. 2012; 107: 2352-2365Crossref PubMed Scopus (0) Google Scholar, 10Quian Quiroga R. Snyder L.H. Batista A.P. Cui H. Andersen R.A. Movement intention is better predicted than attention in the posterior parietal cortex.J. Neurosci. 2006; 26: 3615-3620Crossref PubMed Scopus (92) Google Scholar]. Whereas sensory areas represent the contralateral space, and motor cortex the contralateral limb, PPC represents both limbs and both spatial hemifields, and thus a single implant in one cerebral hemisphere can be used for decoding bimanual operations across space. Third, sequences of movements are represented in PPC [11Baldauf D. Cui H. Andersen R.A. The posterior parietal cortex encodes in parallel both goals for double-reach sequences.J. Neurosci. 2008; 28: 10081-10089Crossref PubMed Scopus (0) Google Scholar]. Most motor behaviors require a sequence of individual movements which can be read out at once in PPC. And fourth, LFP power is very strong in PPC [12Stetson C. Andersen R.A. The parietal reach region selectively anti-synchronizes with dorsal premotor cortex during planning.J. Neuroscience. 2014; (In press)PubMed Google Scholar]. The LFP signal can be used to improve decoding performance when combined with spiking activity and is often complementary to spiking activity [13Hwang E.J. Andersen R.A. The utility of multichannel local field potentials for brain-machine interfaces.J. Neural Eng. 2013; 10: 046005Crossref Scopus (0) Google Scholar]. It is possible that LFP recordings remain robust for longer periods of time than spike recordings. If this is the case, then LFPs recorded from PPC can be used to substantially extend the recording lifetime of implants, currently a major hurdle in the field. The PPC is a source of intention signals which represent plans or potential plans for movement. Moreover, there is a topographic map of intentions within the PPC of both non-human primates and humans [14Andersen R.A. Andersen K.N. Hwang E.J. Hauschild M. Optic ataxia: from Balint's syndrome to the parietal reach region.Neuron. 2014; 81: 967-983Abstract Full Text Full Text PDF PubMed Scopus (0) Google Scholar, 15Andersen R.A. Cui H. Intention, action planning, and decision making in parietal-frontal circuits.Neuron. 2009; 63: 568-583Abstract Full Text Full Text PDF PubMed Scopus (245) Google Scholar]. The reach representation consists of a complex of areas. Much of this complex was originally defined as the parietal reach region (PRR) and includes the medial bank of the IPS and the anterior bank of the parieto-occipital sulcus (POS). Subsequent studies have further divided these regions into V6a (in the POS), and MIP and area 5v in the medial IPS. Another part of this reach complex is dorsal Brodmann’s area 5 (area 5d). Also in PPC is a grasp-specific area, the anterior intraparietal area (AIP), and a saccade-specific area, the lateral intraparietal area (LIP). The PRR was first described in non-human primate studies as a cortical region that is more active for reaches than saccades [16Snyder L.H. Batista A.P. Andersen R.A. Coding of intention in the posterior parietal cortex.Nature. 1997; 386: 167-170Crossref PubMed Scopus (707) Google Scholar]. It strongly represents visual or auditory targets as goals for reaching, predominantly in eye-coordinates [17Batista A.P. Buneo C.A. Snyder L.H. Andersen R.A. Reach plans in eye-centered coordinates.Science. 1999; 285: 257-260Crossref PubMed Scopus (480) Google Scholar, 18Cohen Y.E. Andersen R.A. Reaches to sounds encoded in an eye-centered reference frame.Neuron. 2000; 27: 647-652Abstract Full Text Full Text PDF PubMed Google Scholar]. The effector specificity for goals in PRR is bilateral, with some cells representing reaches with the contralateral limb, others for the ispilateral limb, and many cells for both limbs [9Chang S.W. Snyder L.H. The representations of reach endpoints in posterior parietal cortex depend on which hand does the reaching.J. Neurophysiol. 2012; 107: 2352-2365Crossref PubMed Scopus (0) Google Scholar]. This area represents potential reach plans, prior to a decision, as well as the outcome of the decision [19Cui H. Andersen R.A. Different representations of potential and selected motor plans by distinct parietal areas.J. Neurosci. 2011; 31: 18130-18136Crossref PubMed Scopus (37) Google Scholar]. The robust goal and decision making features should translate into PRR being able to specify a reach more quickly than motor cortex, because of its rich source of information about the final goal of a movement. Non-human primates can use the planning activity in PRR for positioning a cursor on a computer screen under brain control [7Musallam S. Corneil B.D. Greger B. Scherberger H. Andersen R.A. Cognitive control signals for neural prosthetics.Science. 2004; 305: 258-262Crossref PubMed Scopus (463) Google Scholar]. PRR can also represent simultaneously two goals and their sequence in a sequential reach task [11Baldauf D. Cui H. Andersen R.A. The posterior parietal cortex encodes in parallel both goals for double-reach sequences.J. Neurosci. 2008; 28: 10081-10089Crossref PubMed Scopus (0) Google Scholar]. Thus it is an excellent source of reach goal information for BMI applications. Like non-human primates, humans have a complex of reach selective areas in the PPC as demonstrated by fMRI, MEG, and TMS studies in healthy subjects and neuropsychological studies in patients with brain lesions. This complex includes two prominent regions, one located in the medial intraparietal sulcus, and the other more medially in partially overlapping regions referred to as the precuneous, parieto-occipital junction, and superior parietal occipital cortex (Figure 1) [20Astafiev S.V. Shulman G.L. Stanley C.M. Snyder A.Z. Van Essen D.C. Corbetta M. Functional organization of human intraparietal and frontal cortex for attending, looking, and pointing.J. Neurosci. 2003; 23: 4689-4699Crossref PubMed Google Scholar, 21Cavina-Pratesi C. Monaco S. Fattori P. Galletti C. McAdam T.D. Quinlan D.J. Goodale M.A. Culham J.C. Functional magnetic resonance imaging reveals the neural substrates of arm transport and grip formation in reach-to-grasp actions in humans.J. Neurosci. 2010; 30: 10306-10323Crossref PubMed Scopus (159) Google Scholar, 22Connolly J.D. Andersen R.A. Goodale M.A. FMRI evidence for a 'parietal reach region' in the human brain.Exp. Brain Res. 2003; 153: 140-145Crossref PubMed Scopus (0) Google Scholar, 23Filimon F. Nelson J.D. Huang R.S. Sereno M.I. Multiple parietal reach regions in humans: cortical representations for visual and proprioceptive feedback during on-line reaching.J. Neurosci. 2009; 29: 2961-2971Crossref PubMed Scopus (0) Google Scholar, 24Gallivan J.P. McLean D.A. Smith F.W. Culham J.C. Decoding effector-dependent and effector-independent movement intentions from human parieto-frontal brain activity.J. Neurosci. 2011; 31: 17149-17168Crossref PubMed Scopus (63) Google Scholar, 25Prado J. Clavagnier S. Otzenberger H. Scheiber C. Kennedy H. Perenin M.T. Two cortical systems for reaching in central and peripheral vision.Neuron. 2005; 48: 849-858Abstract Full Text Full Text PDF PubMed Scopus (215) Google Scholar, 26Vesia M. Crawford J.D. Specialization of reach function in human posterior parietal cortex.Exp. Brain Res. 2012; 221: 1-18Crossref PubMed Scopus (0) Google Scholar]. Lesions in these regions produce optic ataxia, manifesting as inaccuracies in reach to visual targets in the periphery, largely in eye coordinates [27Dijkerman H.C. McIntosh R.D. Anema H.A. de Haan E.H. Kappelle L.J. Milner A.D. Reaching errors in optic ataxia are linked to eye position rather than head or body position.Neuropsychologia. 2006; 44: 2766-2773Crossref PubMed Scopus (0) Google Scholar, 28Karnath H.O. Perenin M.T. Cortical control of visually guided reaching: evidence from patients with optic ataxia.Cereb. Cortex. 2005; 15: 1561-1569Crossref PubMed Scopus (0) Google Scholar, 29Blangero A. Ota H. Rossetti Y. Fujii T. Ohtake H. Tabuchi M. Vighetto A. Yamadori A. Vindras P. Pisella L. Systematic retinotopic reaching error vectors in unilateral optic ataxia.Cortex. 2010; 46: 77-93Abstract Full Text Full Text PDF PubMed Scopus (0) Google Scholar]. Inactivation of PRR in non-human primates also produces optic ataxia [30Hwang E.J. Hauschild M. Wilke M. Andersen R.A. Inactivation of the parietal reach region causes optic ataxia, impairing reaches but not saccades.Neuron. 2012; 76: 1021-1029Abstract Full Text Full Text PDF PubMed Scopus (31) Google Scholar], and as mentioned above, neurophysiological studies indicate that PRR represents reach targets in eye coordinates, thus giving further evidence for similarities between these regions in the two primate species. A practical difficulty in using the above two candidates of human PRR for neuroprosthetic applications is that these regions are located largely within sulci. Current FDA-approved microelectrode arrays for human implant are the silicon-based arrays produced by Blackrock Microsystems, often referred to as ‘Utah’ arrays, as they were originally developed by Normann and colleagues at the University of Utah [31Campbell P.K. Jones K.E. Huber R.J. Horch K.W. Normann R.A. A silicon-based, three-dimensional neural interface: manufacturing processes for an intracortical electrode array.IEEE Trans. Biomed. Eng. 1991; 38: 758-768Crossref PubMed Scopus (469) Google Scholar]. These arrays, like most silicon-based arrays, are of limited length and cannot reach these deeper areas. This issue of electrode lengths is important to address with neuroengineering and regulatory efforts as it is problematic for many cortical areas with intent signals. Longer silicon-based arrays and microwire-based arrays have been developed (for example [32Huang, R., Changlin, P., Yu-Chong, T., Emken, J., Ustun, C., and Andersen, R. (2008). Parylene coated silicon probes for neural prosthesis. In Proc. 3rd IEEE Int. Conf. on Nano/Micro Eng. Molec. Sys., pp. 947–950.Google Scholar, 33Musallam S. Bak M.J. Troyk P.R. Andersen R.A. A floating metal microelectrode array for chronic implantation.J. Neurosci. Methods. 2007; 160: 122-127Crossref PubMed Scopus (0) Google Scholar]) but these longer arrays do not have FDA approval. Approximately two-thirds of the cortex is located in sulci; for instance, the hand representation of motor cortex is located deep within the central sulcus. One of the areas of the reach complex, area 5d, is located on the gyral surface in both non-human primates and humans (Figure 1). It has the important feature of carrying signals related to both the goal of a movement and the trajectory. The trajectory signal, based on its timing and on the deficits resulting from lesions to this area, appears to be an efference copy of motor commands, and may be used as a forward model for state estimation [34Battaglia-Mayer A. Ferrari-Toniolo S. Visco-Comandini F. Archambault P.S. Saberi-Moghadam S. Caminiti R. Impairment of online control of hand and eye movements in a monkey model of optic ataxia.Cereb. Cortex. 2013; 23: 2644-2656Crossref PubMed Scopus (0) Google Scholar, 35Mulliken G.H. Musallam S. Andersen R.A. Forward estimation of movement state in posterior parietal cortex.Proc. Natl. Acad. Sci. USA. 2008; 105: 8170-8177Crossref PubMed Scopus (99) Google Scholar]. Unlike PRR, area 5d only represents the movement plan that is the outcome of the decision, and not potential plans [19Cui H. Andersen R.A. Different representations of potential and selected motor plans by distinct parietal areas.J. Neurosci. 2011; 31: 18130-18136Crossref PubMed Scopus (37) Google Scholar]. Also it largely represents reach targets in limb coordinates [36Bremner L.R. Andersen R.A. Coding of the reach vector in parietal area 5d.Neuron. 2012; 75: 342-351Abstract Full Text Full Text PDF PubMed Scopus (0) Google Scholar]. Area 5d neurons have been used in BMI applications in which trajectory decoding is used to move a cursor on a computer screen by a non-human primate [8Mulliken G.H. Musallam S. Andersen R.A. Decoding trajectories from posterior parietal cortex ensembles.J. Neurosci. 2008; 28: 12913-12926Crossref PubMed Scopus (0) Google Scholar, 37Hauschild M. Mulliken G.H. Fineman I. Loeb G.E. Andersen R.A. Cognitive signals for brain-machine interfaces in posterior parietal cortex include continuous 3D trajectory commands.Proc. Natl. Acad. Sci. USA. 2012; 109: 17075-17080Crossref PubMed Scopus (0) Google Scholar]. Interestingly, this trajectory decoding can be accomplished without any actual limb movement, so it appears to represent the forward model of an imagined movement or gated motor command. The decoding was accomplished in three-dimensional space and with or without eye fixation [37Hauschild M. Mulliken G.H. Fineman I. Loeb G.E. Andersen R.A. Cognitive signals for brain-machine interfaces in posterior parietal cortex include continuous 3D trajectory commands.Proc. Natl. Acad. Sci. USA. 2012; 109: 17075-17080Crossref PubMed Scopus (0) Google Scholar]. These studies show that area 5d is also a very good location for extracting control signals for neural prosthetics. Recently we have implanted one human tetraplegic patient with a 96 electrode silicon-based ‘Utah’ array (Blackrock Microsystems) in presumed human area 5d based on fMRI localization of imagined reaches. The subject, over a period of recordings of one year, is able to control the three-dimensional endpoint of cursors on a computer screen and the hand location of a robotic limb [38Aflalo T. Kellis S. Klaes C. Lee B. Shi Y. Pejsa K. Shanfield S. Hayes J. S. Aisen M. Heck C. et al.Encoding of spatial information at the level of single units in the human posterior parietal cortex.Soc. Neurosci. Abst. 2014; Google Scholar, 39Kellis S. Klaes C. Aflalo T. Lee B. Shi Y. Pejsa K. Shanfield K. Hayes J. S. Aisen M. Heck C. et al.Brain-machine interface using human parietal cortex: control of prosthetic devices from anterior intraparietal area and Brodmann’s area 5.Soc. Neurosci. Abst. 2014; Google Scholar, 40Klaes C. Kellis S. Aflalo T. Lee B. Shi Y. Pejsa K. Shanfield K. Hayes J. S. Aisen M. Heck C. et al.Grasp representations in the human posterior parietal cortex.Soc. Neurosci. Abst. 2014; Google Scholar]. The anterior intraparietal area (AIP) is a region in the anterior aspect of the IPS, the cells of which are selective for objects and the hand postures to grasp them [41Baumann M.A. Fluet M.C. Scherberger H. Context-specific grasp movement representation in the macaque anterior intraparietal area.J. Neurosci. 2009; 29: 6436-6448Crossref PubMed Scopus (115) Google Scholar, 42Lehmann S.J. Scherberger H. Reach and gaze representations in macaque parietal and premotor grasp areas.J. Neurosci. 2013; 33: 7038-7049Crossref PubMed Scopus (22) Google Scholar, 43Murata A. Gallese V. Luppino G. Kaseda M. Sakata H. Selectivity for the shape, size, and orientation of objects for grasping in neurons of monkey parietal area AIP.J. Neurophysiol. 2000; 83: 2580-2601Crossref PubMed Google Scholar]. Inactivation of AIP produces misshaping of the fingers during grasping [44Gallese V. Murata A. Kaseda M. Niki N. Sakata H. Deficit of hand preshaping after muscimol injection in monkey parietal cortex.Neuroreport. 1994; 5: 1525-1529Crossref PubMed Google Scholar]. In humans, fMRI and TMS studies have identified what appears to be a homologous area specialized for grasping within the anterior IPS at the junction with the postcentral sulcus (Figure 1) [21Cavina-Pratesi C. Monaco S. Fattori P. Galletti C. McAdam T.D. Quinlan D.J. Goodale M.A. Culham J.C. Functional magnetic resonance imaging reveals the neural substrates of arm transport and grip formation in reach-to-grasp actions in humans.J. Neurosci. 2010; 30: 10306-10323Crossref PubMed Scopus (159) Google Scholar, 45Culham J.C. Danckert S.L. DeSouza J.F. Gati J.S. Menon R.S. Goodale M.A. Visually guided grasping produces fMRI activation in dorsal but not ventral stream brain areas.Exp. Brain Res. 2003; 153: 180-189Crossref PubMed Scopus (369) Google Scholar, 46Vesia M. Bolton D.A. Mochizuki G. Staines W.R. Human parietal and primary motor cortical interactions are selectively modulated during the transport and grip formation of goal-directed hand actions.Neuropsychologia. 2013; 51: 410-417Crossref PubMed Scopus (20) Google Scholar]. The same patient that we implanted with a Utah array in presumed area 5d also had an additional 96 electrode array implanted in presumed human AIP [38Aflalo T. Kellis S. Klaes C. Lee B. Shi Y. Pejsa K. Shanfield S. Hayes J. S. Aisen M. Heck C. et al.Encoding of spatial information at the level of single units in the human posterior parietal cortex.Soc. Neurosci. Abst. 2014; Google Scholar, 39Kellis S. Klaes C. Aflalo T. Lee B. Shi Y. Pejsa K. Shanfield K. Hayes J. S. Aisen M. Heck C. et al.Brain-machine interface using human parietal cortex: control of prosthetic devices from anterior intraparietal area and Brodmann’s area 5.Soc. Neurosci. Abst. 2014; Google Scholar, 40Klaes C. Kellis S. Aflalo T. Lee B. Shi Y. Pejsa K. Shanfield K. Hayes J. S. Aisen M. Heck C. et al.Grasp representations in the human posterior parietal cortex.Soc. Neurosci. Abst. 2014; Google Scholar]. Although AIP is located primarily within the intraparietal sulcus in humans and non-human primates, in humans a portion of AIP extends onto the gyral surface. We found that the cells recorded from the AIP array are selective for a variety of hand postures and these signals were used for online grasping of objects with a robot hand under brain control. A special feature of the AIP activity is that a number of grasp postures can be decoded using a small set of AIP neurons. The lateral intraparietal area (LIP) is located on the lateral wall of the posterior half of the IPS in non-human primates. Like the PRR, it codes goal locations in eye-centered coordinates, but for saccades rather than reaches [16Snyder L.H. Batista A.P. Andersen R.A. Coding of intention in the posterior parietal cortex.Nature. 1997; 386: 167-170Crossref PubMed Scopus (707) Google Scholar]. While we are not always looking toward where we are reaching, the overall statistics of the patterns of eye-hand movements can be used to improve decoding of reach activity from PRR by providing an additional channel of information [47Pesaran, B., and Andersen, R.A. (2010). Prosthetic devices and methods and systems related thereto, Issued Sept 14, 2010. Volume Patent #US 7,797,040.Google Scholar]. Thus, recordings from LIP can be used to improve decoding of reach targets when combined with PRR activity during free gaze and eye-hand coordination. Area LIP neural populations provide an accurate on-line coding of both the direction of eye movements and current eye position [48Graf A.B. Andersen R.A. Inferring eye position from populations of lateral intraparietal neurons.Elife. 2014; 3: e02813Crossref Scopus (0) Google Scholar]. Additionally, it has been shown that LIP activity can be used in a brain control task in which eye movements are planned but not executed [49Graf A. Andersen R. Learning to infer eye movement plans from populations of intraparietal neurons.COSYNE. 2013; 2013: 33-34Google Scholar]. Thus LIP can serve as a substitute to bulky eye monitoring equipment used for tetraplegic patients, and as a primary communication channel for locked-in subjects. LIP has also been determined to be a primary site for learned categorization of visual stimuli [50Freedman D.J. Assad J.A. Experience-dependent representation of visual categories in parietal cortex.Nature. 2006; 443: 85-88Crossref PubMed Scopus (253) Google Scholar, 51Freedman D.J. Assad J.A. A proposed common neural mechanism for categorization and perceptual decisions.Nat. Neurosci. 2011; 14: 143-146Crossref PubMed Scopus (63) Google Scholar]. The categorization function pertains to a variety of stimulus attributes, including motion direction and shape. Interestingly this categorization function is stronger and appears earlier when directly compared to the dorsal prefrontal cortex, an area previously implicated in categorization [52Swaminathan S.K. Freedman D.J. Preferential encoding of visual categories in parietal cortex compared with prefrontal cortex.Nat. Neurosci. 2012; 15: 315-320Crossref PubMed Scopus (77) Google Scholar]. It also appears more robust than categorization-related activity in inferotemporal cortex. Thus LIP is a possible site for using a computer tablet where the desired visual icon is directly categorized and decoded. This direct method would bypass the need for “move mouse and click” operations for tablet use. A presumed homologue of LIP has been identified in humans on the medial bank in the middle of the IPS (Figure 1) [53Culham J.C. Cavina-Pratesi C. Singhal A. The role of parietal cortex in visuomotor control: what have we learned from neuroimaging?.Neuropsychologia. 2006; 44: 2668-2684Crossref PubMed Scopus (0) Google Scholar]. Like the PPC, there are a number of areas in premotor cortex that can serve as sources of signals for neuroprosthetics. And also like PPC, the premotor cortex contains a map of intentions with the dorsal premotor cortex (PMd) selective for reach, ventral premotor cortex (PMv) for grasp, and the frontal eye fields (FEF) for saccades. Not surprisingly, the predominant reciprocal parieto-frontal connections of these areas are with PPC areas showing similar selectivity: PMd with PRR and area 5d; PMv with AIP; and FEF with LIP [42Lehmann S.J. Scherberger H. Reach and gaze representations in macaque parietal and premotor grasp areas.J. Neurosci. 2013; 33: 7038-7049Crossref PubMed Scopus (22) Google Scholar, 54Andersen R.A. Asanuma C. Essick G. Siegel R.M. Corticocortical connections of anatomically and physiologically defined subdivisions within the inferior parietal lobule.J. Comp" @default.
• W1998916618 created "2016-06-24" @default.
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• W1998916618 date "2014-09-01" @default.
• W1998916618 modified "2023-10-15" @default.
• W1998916618 title "Toward More Versatile and Intuitive Cortical Brain–Machine Interfaces" @default.
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